arXiv subjects

Simon de Wergifosse

Publications and source records attributed to Simon de Wergifosse.

6 recordsLinked to original sources

arXiv2026

The Deformed Image Vortex Ansatz: A Perturbation-Aware Description of Magnetic Vortices in In-Plane Fields

Thiele-based descriptions of magnetic vortex dynamics in thin ferromagnetic nanodots rely on magnetization ansätze that describe the equilibrium texture but cannot represent perturbation-induced deformations. We introduce the Deformed Image Vortex Ansatz (DIVA): a perturbation-aware ansatz in which the response to an external perturbation is built into the magnetization profile itself, rather than appended to the dynamics as a correction. Here, we demonstrate the concept for a uniform, stationary in-plane field applied to a Permalloy nanodot, for which the deformation is analytically tractable. A symmetry-based perturbative expansion identifies the leading deformation as a single $m = 1$ harmonic around the disk, while energy minimization and a dominant-balance analysis yield a closed-form interpolant for the radial profile. Benchmarked against micromagnetic simulations on Permalloy disks of aspect ratio $t/R = 0.1$ and $0.0125$, this realization reduces the disk-averaged angular deviation by a factor of 3 to 6 relative to the two-vortex ansatz, depending on geometry and field, and reduces the total-energy deviation by about a factor of six in the thicker disk.

cond-mat.mes-hall↗

arXiv2026

Spintronics for image recognition: performance benchmarking via data-driven simulations

We present a demonstration of image classification using an extreme learning machine (ELM) based on a unique simulated magnetic tunnel junction (MTJ) delayed in time. As the ground state of the MTJ is a magnetic vortex, we refer to it as a vortex-based spin-torque oscillator (STVO). The dynamics of the magnetic vortex is simulated with a model called the data-driven Thiele equation approach (DD-TEA). This allows to avoid the constraints associated with repeated experimental manipulation for hyperparameters search and benchmarking. We showcase the versatility of our implementation by using it successfully for classification tasks on the MNIST, EMNIST-letters and Fashion MNIST datasets. Through simulations, we show that within an ELM with a sufficient number of parameters, the performance reached using the STVO dynamics as a source of nonlinearity is equivalent to the ones obtained with classical software activation functions such as the reLU and the sigmoid. While achieving state-of-the-art accuracy levels on the MNIST dataset, our model's performance on EMNIST-letters and Fashion MNIST is lower due to the simplicity of the network architecture and the increased complexity of the data. We expect that the DD-TEA framework will enable the exploration of deeper and more complex STVO-based architectures, ultimately leading to improved classification accuracy.

cs.CV↗

arXiv2024

Geometrical properties of 3D crossed nanowire networks

Three-dimensional interconnected nanowire networks have recently attracted notable attention for the fabrication of new devices for energy harvesting/storage, sensing, catalysis, magnetic and spintronic applications and for the design of new hardware neuromorphic computing architectures. However, the complex branching of these nanowire networks makes it challenging to investigate these 3D nanostructured systems theoretically. Here, we present a theoretical description and simulations of the geometric properties of these 3D interconnected nanowire networks with selected characteristics. Our analysis reveals that the nanowire segment length between two crossing zones follows an exponential distribution. This suggests that shorter nanowire segments have a more pronounced influence on the nanowire network properties compared to their longer counterparts. Moreover, our observations reveal a homogeneous distribution in the smallest distance between the cores of two crossing nanowires. The results are highly reproducible and unaffected by changes in the nanowire network characteristics. The density of crossing zones and interconnected nanowire segments are found to vary as the square of the nanowire density multiplied by their diameter, further multiplied by a factor dependent on the packing factor. Finally, densities of interconnected segments up to 10$^{13}$ cm$^{-2}$ can be achieved for 22-$μ$m-thick nanowire networks with high packing factors. This has important implications for neuromorphic computing applications, suggesting that the realization of 10$^{14}$ interconnections, which corresponds to the approximate number of synaptic connections in the human brain, is achievable with a nanowire network of about 10 cm$^{2}$.

physics.app-ph↗

arXiv2023

Current-controlled periodic double-polarity reversals in a spin-torque vortex oscillator

Micromagnetic simulations are used to study a spin-torque vortex oscillator excited by an out-of-plane dc current. The vortex core gyration amplitude is confined between two orbits due to periodical vortex core polarity reversals. The upper limit corresponds to the orbit where the vortex core reaches its critical velocity triggering the first polarity reversal which is immediately followed by a second one. After this double polarity reversal, the vortex core is on a smaller orbit that defines the lower limit of the vortex core gyration amplitude. This double reversal process is a periodic phenomenon and its frequency as well as the upper and lower limits of the vortex core gyration are controlled by the input current density while the vortex chirality determines the onset of this confinement regime. In this non-linear regime, the vortex core never reaches a stable orbit and thus, it may be of interest for neuromorphic application, for example as a leaky integrate-and-fire neuron.

cond-mat.mes-hall↗

arXiv2023

Neuromorphic spintronics simulated using an unconventional data-driven Thiele equation approach

In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS). Solving the STVO dynamics with our analytical model allows to accelerate the simulations by 9 orders of magnitude compared to MMS while reaching the same level of accuracy. Here, we showcase our model by simulating a STVO-based neural network for solving a classification task. We assess its performance with respect to the input signal current intensity and the level of noise that might affect such a system. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices while decreasing drastically its computational cost.

cs.CV↗

arXiv2023

A semi-analytical model to simulate the spin-diode effect and accelerate its use in neuromorphic computing

The spin-diode effect is studied both experimentally and with our original semi-analytical method. The latter is based on an improved version of the Thiele equation approach (TEA) that we combine to micromagnetic simulation data to accurately model the non-linear dynamics of spin-torque vortex oscillator (STVO). This original method, called data-driven Thiele equation approach (DD-TEA), absorbs the difference between the analytical model and micromagnetic simulations to provide a both ultra-fast and quantitative model. The DD-TEA model predictions also agree very well with the experimental data. The reversal of the spin-diode effect with the chirality of the vortex, the impact of the input current and the origin of a variation at half of the STVO frequency are presented as well as the ability of the model to reproduce the experimental behavior. Finally, the spin-diode effect and its simulation using the DD-TEA model are discussed as a promising perspective in the framework of neuromorphic computing.

cond-mat.mes-hall↗